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Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data (Quantitative and Network Biology (VCH))

Wiley-Blackwell

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ISBN 10: 3527332626 / ISBN 13: 9783527332625
Published by Wiley-Blackwell, 2013
Used Condition: Good Hardcover
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Bibliographic Details

Title: Statistical Diagnostics for Cancer: ...

Publisher: Wiley-Blackwell

Publication Date: 2013

Binding: Hardcover

Book Condition:Good

Edition: 1.

About this title

Synopsis:

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

From the Back Cover:

This ready reference discusses different methods for statistically analyzing and validating cancer data generated from high-throughput methods. In contrast to other titles, this book focuses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or
less complex biological network. Due to the complex nature of cancer, such approaches are very appropriate.

From a methodological point of view, the well-balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of suffi cient computer power in the recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches such as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, Bayesian methods are discussed, because these are particularly useful for a systematic data integration. Thus, the book serves as a useful guide for methods-oriented scientists and the medical and basic research community.

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